Seaweed aquaculture, the fastest-growing component of global food production, offers a slate of opportunities to mitigate and adapt to climate change. Seaweed farms release carbon that maybe buried in sediments or exported to the deep sea, therefore acting as a CO2 sink. The crop can also be used, in total or in part, for biofuel production, with a potential CO2 mitigation capacity, in terms of avoided emissions from fossil fuels, of about 1500 tons CO2 km-2 year-1. Seaweed aquaculture can also help reduce the emissions from agriculture, by improving soil quality substituting synthetic fertilizer and, when included in cattle fed, lowering methane emissions from cattle. Seaweed aquaculture contributes to climate change adaptation by damping wave energy and protecting shorelines, and by elevating pH and supplying oxygen to the waters, thereby locally reducing the effects of ocean acidification and de-oxygenation. The scope to expand seaweed aquaculture is, however, limited by the availability of suitable areas and competition for suitable areas with other uses, engineering systems capable of coping with rough conditions offshore and an increasing market demand for seaweed products, among other factors. Despite these limitations, seaweed farming practices can be optimized to maximize climate benefits, which may, if economically compensated, improve the income of seaweed farmers.
Geological remote sensing interpretation enables the extraction of task-specific geological information from imagery, playing a vital role in geological investigation and analysis. Nevertheless, deep learning-based methods still lag behind expert visual interpretation in terms of accuracy, owing to the high spectral similarity, fragmented spatial distributions, and class imbalance of geological elements. This study proposes a multilevel feature integration network (MLFIN) to address the above challenges in multiclass geological element interpretation. Specifically, a class-wise random sampling strategy is designed to alleviate class distribution imbalance, improving the capability of MLFIN to learn features from diverse geological elements. The encode–decode modules can enhance feature representation and improve the utilization of element information. The former integrates convolution operations with attention mechanisms to extract multiscale spatial–spectral features, while the latter enables the adaptive fusion of multilevel features. To mitigate the boundary ambiguity segmentation, the global attention module models long-range contextual dependencies, while the segmentation module refines spatial features, enriching the spatial details of geological elements and strengthening boundary feature discrimination. Experimental results on two geological datasets indicate that MLFIN outperforms current deep learning-based segmentation methods, achieving Overall Accuracy value of 72.83% and 67.36%, and mean Intersection-over-Union values of 55.6% and 46.16%, respectively. It validates the strong generalization capacity across diverse geological environments and proves the effectiveness of its submodule in multiclass geological interpretation tasks.
In this paper, our aim is to further expand the use of the two-variable $ (G '/G, 1/G) $ (G′/G,1/G)-expansion approach to a new coupled KdV and Z-K system, which has various significant applications in different fields of applied sciences. The KdV equation, along with shallow-water waves and long internal waves in oceans, basically explains how long, one-dimensional waves propagate in a variety of physical conditions. The study of coastal waves on the basis of the ocean is done using the Zakharov–Kuznetsov (Z-K) equation and this model is utilized to illustrate ion-acoustic wave propagation. By using this method, different forms of analytical solutions of the new coupled KdV (NCKdV) system and the new coupled Z-K (NCZ-K) system, such as solitons, multi-peak solitons, solitary waves, trigonometric, hyperbolic and rational functions and other wave solutions are constructed. The significant features of multi-peak solitons induced by the higher-order effects, including velocity variations, localization or periodicity attenuation and state transitions, are revealed. When the localization disappears then the multi-peak soliton becomes a periodic wave. The constructed solutions are also presented graphically having their applications in engineering, etc. The stability of the solution is examined by utilizing modulation instability. The results obtained show that the proposed technique is universal and efficient. In addition, this technique can also be applied to lots of other new coupled systems arising in other areas of applied sciences.
Ice gouging is a significant issue for offshore structures in cold environments. Pipelines in Arctic regions are buried in the seabed to prevent the direct contact of pipelines and the impacts of soil displacement from ice gouging. However, choosing the appropriate backfilling material and stiffness to maintain the pipeline's integrity while minimizing construction costs is a complex design consideration. It is crucial to accurately model the interaction between the ice, backfill, trench wall, and pipeline to assess the backfill functionality in a coupled ice gouging analysis. This study comprehensively investigated the effect of backfilling stiffness and configuration on seabed failure mechanisms and pipeline response during ice gouging events on a deeply buried pipeline. The study focused on six different backfill materials, including dense and loose sands and very soft clay to stiff clay. The Coupled Eulerian-Lagrangian (CEL) method was used to simulate the large seabed deformation due to the ice gouging process in a trenched/backfilled seabed in Abaqus/Explicit. Incorporation of the strain-rate dependency and strain-softening effects involved the development of a user-defined subroutine and incremental update of the undrained shear strength within the Abaqus software. Key findings reveal that both overly soft and excessively stiff backfill materials can negatively impact pipeline response during ice gouging. Very soft clay exhibits a distinct ''removal'' mechanism, leading to increased pipeline displacement, while overly stiff clay and dense sands result in more significant displacement due to efficient force transfer. The results can inform the selection of appropriate backfill materials and backfilling techniques to enhance pipeline protection against ice gouging.
Convolutional neural networks (CNNs) focus solely on extracting local features, lacking the ability to capture global spectral-spatial information. Meanwhile, Transformers effectively learn the overall distribution and mutual relationships of spectral features but overlook the extraction of local spatial features. To fully leverage the complementary advantages of both techniques, the article proposes a re-attention agent transformer and multiscale partial convolution (RAT-MPC) for hyperspectral image classification. It effectively utilizes the local learning capability of CNNs and the long-range modeling ability of Transformers. Specifically, the multiscale spatial-spectral feature learning module employs a strategy of split, refactoring, fusion to extract shallow feature information. Subsequently, the dual branch feature processing module handles the obtained features from both local and global perspectives. On one hand, the re-attention agent transformer branch is employed to learn complex global spectral relationships. On the other hand, multiscale partial convolutions are utilized to further learn abstract spatial features. Finally, the multilevel feature fusion attention module is designed to fully use features from different receptive fields and depths. In addition, it incorporates an enhanced coordinate attention mechanism to reinforce spatial detail features. To evaluation the proposed RAT-MPC effectiveness, 5%, 0.7%, and 0.1% of labeled samples are selected from the Indian Pines (IP), Pavia University (PU), and WHU-Hi-LongKou (LK) datasets, respectively. The experimental results demonstrate that the proposed network exhibited exceptional classification performance, achieving overall accuracies of 96.66%, 98.20%, and 98.44% on the IP, PU, and LK datasets, respectively. Compared with the latest CNN-Transformer related method DBCTNet, the proposed method achieves improvements of 1.36%, 0.68%, and 1.38% in overall accuracies, respectively.
The detection and cultivation of circulating tumor cells (CTCs) play a crucial role in monitoring tumor recurrence, metastasis, early disease diagnosis, and assessing the effectiveness of drug treatments. This study specifically focused on investigating human breast cancer cells MCF-7 by utilizing framework nucleic acids (FNAs) as bio-probe scaffold in conjunction with fishbone structures and three-dimensional (3D) microcavity structures within microchannels. These components collectively formed an integrated nano-micro interface system designed for a comprehensive examination of CTC detection and cell culture. The study involved the assessment and comparison of rigid 3D FNAs with distinct side lengths of 7, 13, and 26 bases. This approach not only allowed for precise regulation of the DNA biosensor interface through the manipulation of probe spacing, facilitating optimal probe-cell interactions within the microfluidic channel. Consequently, this approach significantly enhances capture efficiency and lowers the CTC detection limit to 5 cells/mL. Moreover, this research successfully observed cell proliferation and individual cell biological behavior within the 3D microcavity structure. The findings indicated that the overall cell population's proliferation was like that in static culture conditions. Although the proliferation cycle of individual cells was notably extended, cell mobility within the microcavity demonstrated their robust biological activity. These significant outcomes not only offer a practical approach for early tumor detection but also provide a valuable pathway for comprehending mechanisms of tumor development and advancement and guiding personalized treatment strategies effectively.
Ship detection of synthetic aperture radar (SAR) images holds significant value for both civilian and military applications. Compared to horizontal ship detection, oriented ship detection based on oriented bounding boxes can capture the orientation and aspect ratio of ships, thus receiving increasing attention. However, in oriented ship detection, when ships rotate near the specific angles, the angle prediction result obtained by the deep learning network may have a severe mutation, which is the well-known boundary discontinuity problem. To address the issue of boundary discontinuity in SAR ship detection, researchers have proposed numerous methods. However, through our systematic analysis, we found that these methods do not fundamentally solve the problem. To this end, we first clarified the reasons for the existence of boundary discontinuity and how it affects the detection network. Based on this, we proposed the conditions that the encoding methods and loss functions of the detection network must satisfy to address the issue of boundary discontinuity. In line with these conditions, we designed a continuous encoding method called coordinate decomposition method (CDM). In addition, we also analyzed the impact of different optimization methods on the detection network and, based on this, presented a joint optimization paradigm based on continuous encoding. Experimental results on two commonly used SAR ship detection datasets demonstrate that our proposed CDM encoding method effectively addresses the boundary discontinuity issue and enhances the detection performance. Compared to the state-of-the-art methods, the fully convolutional one-stage network using the CDM-based joint optimization achieves optimal detection results without employing any additional techniques.
Due to the high cost and relatively low image quality of hyperspectral sensors, spectral super-resolution seeks to explore the mapping mechanisms between multispectral and hyperspectral images (HSIs), with the goal of reconstructing high-quality HSIs. In recent years, deep learning algorithms have achieved significant success in spectral super-resolution. However, most of these methods compute loss only at the final stage of the network, neglecting the intermediate generative processes, which leads to considerable spectral distortion in the reconstructed images. To address these issues, we propose a progressive conditional diffusion model (PCDM) for multistage spectral restoration. PCDM constructs a channel synthesis module that generates a ground truth set through band synthesis, and designs an image reconstruction module (IRM) to ensure that the synthesized image in the next stage can effectively reconstruct the synthesized features from the previous stage. Multiple conditional diffusion models are then constructed based on the dataset. For each conditional diffusion model, the network parameters of the corresponding IRM are shared with the multispectral image for spectral up-sampling. The spectral-up-sampled multispectral features, combined with the output from the previous diffusion model, serve as a conditional matrix, which is input into the next diffusion model to obtain the final result. Experimental results on both synthetic and real datasets demonstrate that PCDM can effectively reconstruct HSIs, showing robustness and outperforming state-of-the-art methods.
Remote sensing image classification models face significant challenges when adapting to new domains due to variations in image acquisition conditions, sensor types, and scene categories. Conventional domain adaptation methods rely on multistage adaptation pipelines with limited semantic understanding, and even recently developed vision-language models (VLMs) still exhibit limited discriminative capability when encountering unseen images. To tackle these challenges, we propose OSClip, a novel open-set domain adaptation framework based on the VLM model, contrastive language-image pre-training (CLIP). Specifically, OSClip harnesses the powerful generalization capabilities of CLIP by employing domain-adaptive prompt tuning, which inserts lightweight, learnable prompts into both the vision and language encoders. This design enables efficient adaptation to new, unlabeled target domains while retaining knowledge acquired during pretraining. Furthermore, a robust open-set recognition mechanism is incorporated by combining confidence-weighted pseudolabel supervision and energy-based regularization, further strengthened by a teacher–student self-distillation scheme to enhance pseudolabel reliability under unsupervised conditions. To support adaptation across multiple target domains while mitigating catastrophic forgetting, OSClip adopts a continual adaptation paradigm for the blended test set. It dynamically aggregates prompts based on the distribution of domain-specific features to ensure stable knowledge transfer. Extensive experiments on public remote sensing datasets demonstrate that OSClip consistently outperforms state-of-the-art methods, delivering superior accuracy in distinguishing known and unknown classes across various adaptation scenarios. The results also confirm the effectiveness of OSClip in achieving robust cross-modal and cross-domain semantic alignment.
Underwater images always suffer from color distortion, contrast decrease, and detail blur due to the selective absorption and scattering of water, which significantly limits their applications. In this article, we introduce an effective underwater image enhancement method to improve image quality, i.e., correcting color distortion, enhancing contrast, and enriching details. Specifically, we first use a color-cast factor to classify underwater images into no-color-cast and color-cast images; the latter is divided into three color shifts to help apply adaptive color correction approaches for images with different color shifts. Based on the color-corrected results, we enhance their luminance information and enrich details via a well-designed histogram equalization algorithm and a multiscale detail superposition algorithm. We finally combine fused luminance information and corrected color information to output the desired images with improved quality. Experiments on four representative underwater benchmarks validate our method's robustness to different categories of underwater images, as well as its superiority compared with state-of-the-art methods. The proposed underwater imaging solution holds significant potential for practical applications in ocean engineering, e.g., ocean vision, underwater tracking, target identification, and location.
Shahab Rouhi, Setare Sadeqi, Nikolaos I. Xiros
et al.
The primary goal of this study is to develop and test a small-scale horizontal-axis underwater Ocean Current Turbine (OCT) by creating a mathematical model for coupled dynamics aided by a Blade Element Momentum (BEM) simulation-integrated experimental approach. This research is motivated by the urgent need for sustainable energy sources and the vast potential of ocean currents. By integrating mathematical modeling with the experimental testing of scaled model OCTs, this study aims to evaluate performance accurately. The experimental setup involves encapsulating a 3D-printed turbine model within a watertight nacelle which is equipped with sensors for comprehensive data recording during towing tank tests. Through these experiments, we seek to establish correlations between the generated power, force, and rotational speed of the turbine’s Permanent Magnet DC (PMDC) motor, which determines the turbine’s capability to extract dynamic energy inflow. Moreover, this research aims to provide valuable insights into the accuracy and applicability of theoretical predictions in real-world scenarios by comparing the experimental results with BEM simulations. This combined approach not only advances our understanding of hydrokinetic energy conversion, but also contributes to the development of reliable and efficient renewable energy technologies that address global energy challenges while mitigating environmental impacts.
Two accretionary sediment sequences from Sites 1245 and 1252 recovered during Ocean Drilling Program (ODP) Leg 204 at southern Hydrate Ridge were investigated to explore the response of geochemical partitioning of iron and sulfur isotopic composition of authigenic pyrite to non-steady-state depositional and diagenetic scenarios. Five iron species were characterized by a modified sequential extraction procedure that covers almost all iron-bearing minerals in sediment cores, including: (1) iron-bearing carbonates, mainly siderite; (2) ferric (hydr)oxides, probably ferrihydrite and/or lepidocrocite; (3) magnetite; (4) iron-bearing silicates; and (5) pyrite. Highly reactive iron has been accumulated for a long-term steady-state history and its pyritization, to varying degrees, is limited by availability of dissolved sulfide. This causes pyrite and siderite occurred in the same sedimentary layer and shows an inverse relationship between their concentrations. From this, their proportions to highly reactive iron can be chosen for evaluating the degree of sulfidization. A significant change in sulfur isotopic composition of pyrite (-42.4 to +16.8‰ VCDT) indicates that the steady-state conditions are dramatically limited, where the δ34S values higher than -20‰ may result from an upward shift of SMT zone close to the seafloor or a sudden, massive depositional event. To explain the downcore sulfidization effects and pyrite δ34S values, we developed two categories of conceptual scenarios based on variations in sedimentation rate and methane flux. The geochemical features similar to those derived from each scenario were searched in the sediment columns and the non-steady-state events behind the scenarios were proved to be consistent with the real observations. Thus, iron species and pyrite δ34S values can be regarded as a proxy to differentiate different non-steady-state depositional and diagenetic controls on the sedimentary record.
Science, General. Including nature conservation, geographical distribution